In this tutorial, we will explore the intersection of causality and large language models (LLMs). Our goal is to provide a comprehensive understanding of how causal inference can enhance the performance, interpretability, and robustness of LLMs. The tutorial will cover foundational concepts in both fields, discuss emerging trends, present three paradigms for causality for LLM research, and corresponding practical applications. We also include a panel of experts with diverse backgrounds, including Yoshua Bengio, to engage the NeurIPS community with a comprehensive overview and diverse perspectives.